
import time

time_start_notebook = time.time()


%%capture
import sys
ENV_COLAB = 'google.colab' in sys.modules

if ENV_COLAB:
    # usual imports
    !pip install watermark
    !pip install scikit-plot
    !pip install catboost

    # HPO
    !git clone https://github.com/thuijskens/scikit-hyperband.git
    sys.path.append('scikit-hyperband/hyperband')

    print('Environment: Google Colab')


import numpy as np
import pandas as pd
import seaborn as sns
import os,sys,time
import matplotlib.pyplot as plt
import joblib
from tqdm import tqdm, trange
import plotly_express as px

# modelling
import sklearn.metrics as skmetrics
from sklearn.model_selection import StratifiedKFold

# boosting
import xgboost as xgb
import lightgbm as lgb
import catboost as cb
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from catboost import CatBoostClassifier

# settings
sns.set()
SEED = 100
pd.set_option('max_columns',100)
pd.set_option('max_colwidth',200)
pd.set_option('plotting.backend','matplotlib') # matplotlib, bokeh, altair, plotly

%matplotlib inline
%load_ext watermark
%watermark -iv

numpy          1.19.4
xgboost        1.2.0
autopep8       1.5.2
pandas         1.1.4
catboost       0.23.2
joblib         0.17.0
seaborn        0.11.0
lightgbm       2.3.1
plotly_express 0.4.1
json           2.0.9


def show_methods(obj, ncols=4,contains=None):
    lst = [i for i in dir(obj) if i[0]!='_' ]
    if contains is not None:
        lst = [i for i in lst if contains in i]
    df = pd.DataFrame(np.array_split(lst,ncols)).T.fillna('')
    return df


def model_eval_bin(model_name,ytest,ypreds,yprobs2d,show_plots=True):
    import sklearn.metrics as skmetrics
    import scikitplot.metrics as skpmetrics
    import os

    acc       = skmetrics.accuracy_score(ytest,ypreds)
    precision = skmetrics.precision_score(ytest,ypreds)
    recall    = skmetrics.recall_score(ytest,ypreds)
    f1        = skmetrics.f1_score(ytest,ypreds)
    auc       = skmetrics.roc_auc_score(ytest,ypreds)

    print(skmetrics.classification_report(ytest,ypreds))
    print(skmetrics.confusion_matrix(ytest,ypreds))

    df_res = pd.DataFrame({'Accuracy':[acc],
                          'Precision': [precision],
                          'Recall': [recall],
                          'F1-score': [f1],
                          'AUC': [auc]},index=[model_name])

    display(df_res.style.format("{:.4f}"))
    if not os.path.isdir('../outputs'):
        os.makedirs('../outputs')
    o = '.' if ENV_COLAB else '../outputs/'
    df_res.to_csv(o+f'model_{model_name}.csv',index=True)

    if show_plots:
        skpmetrics.plot_precision_recall(ytest,yprobs2d) # more focus on minority
        skpmetrics.plot_roc(ytest,yprobs2d) # equal focus on both groups
        skpmetrics.plot_confusion_matrix(ytest,ypreds)


def get_profit(y_true, y_pred):
    tn, fp, fn, tp = skmetrics.confusion_matrix(y_true,y_pred).ravel()
    profit = 400*tp - 200*fn - 100*fp
    return profit

scoring = skmetrics.make_scorer(get_profit, greater_is_better=True)


path_data_train = '../data/raw/train.csv'
path_data_test = '../data/raw/test.csv'

if ENV_COLAB:
    path_data_train = 'https://raw.githubusercontent.com/bhishanpdl/Datasets/master/Projects/Telco_Customer_Churn/raw/train.csv'
    path_data_test = 'https://raw.githubusercontent.com/bhishanpdl/Datasets/master/Projects/Telco_Customer_Churn/raw/test.csv'


df_train = pd.read_csv(path_data_train)
df_test = pd.read_csv(path_data_test)

print(df_train.shape)
print(df_test.shape)
df_train.head(2).append(df_train.tail(2))

(5634, 21)
(1409, 21)


target_name = 'Churn'


px.histogram(df_train, x=target_name,height=300,width=300)


px.histogram(df_train, x='gender', color=target_name,height=300,width=300)


df_train['TotalCharges'] = pd.to_numeric(df_train['TotalCharges'],errors='coerce').fillna(0)
df_test['TotalCharges'] = pd.to_numeric(df_test['TotalCharges'],errors='coerce').fillna(0)


df_train['SeniorCitizen'] = df_train['SeniorCitizen'].map({0:'No',1:'Yes'})
df_test['SeniorCitizen'] = df_test['SeniorCitizen'].map({0:'No',1:'Yes'})


df_Xtrain = df_train.drop(target_name,axis=1)
df_Xtest = df_test.drop(target_name,axis=1)

ser_ytrain = df_train[target_name].map({'No':0,'Yes':1})
ser_ytest = df_test[target_name].map({'No':0,'Yes':1})

ytrain = np.array(ser_ytrain).flatten()
ytest = np.array(ser_ytest).flatten()

index_name = 'customerID'
ser_train_ids = df_Xtrain.pop(index_name)
ser_test_ids = df_Xtest.pop(index_name)


df_Xtrain.head(2)


cols_num = list(df_train.select_dtypes('number').columns)
cols_num

['tenure', 'MonthlyCharges', 'TotalCharges']


cols_cat = list(df_train.select_dtypes('object').columns)

# gender is no good predictor as seen in EDA
cols_exclude = ['customerID','gender','TotalCharges'] + [target_name]
cols_cat = [ i for i in cols_cat if i not in cols_exclude ] + ['SeniorCitizen']

print(cols_cat)

['SeniorCitizen', 'Partner', 'Dependents', 'PhoneService', 'MultipleLines', 'InternetService', 'OnlineSecurity', 'OnlineBackup', 'DeviceProtection', 'TechSupport', 'StreamingTV', 'StreamingMovies', 'Contract', 'PaperlessBilling', 'PaymentMethod', 'SeniorCitizen']


cols_num = ['TotalCharges','tenure', 'MonthlyCharges']


cols_num_old = cols_num
cols_cat_old = cols_cat


def combine_two_features(dfx,A,B):
    dfx = dfx.copy()
    assert len(A) == len(B)
    for a,b in zip(A,B):
        dfx[a+'_'+b] = dfx[a] + '_' + dfx[b]

    return dfx

combineA = ['Partner']
combineB = ['Dependents']
combineA = combineA + ['SeniorCitizen']*5
combineB = combineB + ['Dependents','Partner','Contract',
                       'TechSupport','PaymentMethod']

cols_cat_new = [f'{a}_{b}' for a,b in zip(combineA,combineB)]


cols_cat = list(set(cols_cat + cols_cat_new))
print(cols_cat_new)
# print(cols_cat)

df_Xtrain = combine_two_features(df_Xtrain,combineA,combineB)
df_Xtest = combine_two_features(df_Xtest,combineA,combineB)

['Partner_Dependents', 'SeniorCitizen_Dependents', 'SeniorCitizen_Partner', 'SeniorCitizen_Contract', 'SeniorCitizen_TechSupport', 'SeniorCitizen_PaymentMethod']


def create_groupby_features(dfx,cat,num,agg):
    dfx = dfx.copy()
    for c in cat:
        for n in num:
            for a in agg:
                name = f"{c}_{n}_{a}"
                dfx[name] = df_train.groupby(c)[n].transform(a)
    return dfx


# Using more features gave me worse AUC.
# cols_grpcat = ['Contract','PaymentMethod']
# cols_grpnum = ['TotalCharges','MonthlyCharges']
# cols_grpagg = ['mean', 'max', 'min']

cols_grpcat = ['Contract']
cols_grpnum = ['TotalCharges']
cols_grpagg = ['mean']

cols_num_new = [f'{c}_{n}_{a}' 
                for c in cols_grpcat
                for n in cols_grpnum
                for a in cols_grpagg]

cols_num = list(set(cols_num + cols_num_new))
print(cols_num_new)
# print(cols_num)

df_Xtrain = create_groupby_features(df_Xtrain,cols_grpcat, cols_grpnum, cols_grpagg)
df_Xtest = create_groupby_features(df_Xtest,cols_grpcat, cols_grpnum, cols_grpagg)

['Contract_TotalCharges_mean']


df_Xtrain.head(2)


cols_drop = ['gender']

df_Xtrain = df_Xtrain.drop(cols_drop,axis=1)
df_Xtest = df_Xtest.drop(cols_drop,axis=1)


all_features = df_Xtrain.columns.tolist()
cols_cat_idx = [all_features.index(i)
                        for i in cols_cat]


# make sure no nans

df_Xtrain.isna().sum().sum(), df_Xtest.isna().sum().sum()

(0, 0)


df_Xtrain_full = df_Xtrain.copy()
ser_ytrain_full = ser_ytrain.copy()
ytrain_full = np.array(ser_ytrain_full).flatten()


# one hot encode
df_Xtrain_full = pd.get_dummies(df_Xtrain_full,columns=cols_cat)
df_Xtest = pd.get_dummies(df_Xtest,columns=cols_cat)


df_Xtrain_full.head()


# check if all nunique >= 2
df_Xtrain_full.apply(pd.Series.nunique).nsmallest(5)

PaperlessBilling_No     2
PaperlessBilling_Yes    2
Dependents_No           2
Dependents_Yes          2
StreamingMovies_No      2
dtype: int64


# check if all nunique >= 2
df_Xtest.apply(pd.Series.nunique).nsmallest(5)

PaperlessBilling_No     2
PaperlessBilling_Yes    2
Dependents_No           2
Dependents_Yes          2
StreamingMovies_No      2
dtype: int64


# check if all are numbers
df_Xtrain_full.sum().sum(), df_Xtest.sum().sum()

(26266765.14999999, 6655873.894557063)


# check for nans
df_Xtrain_full.isna().sum().sum(), df_Xtest.isna().sum().sum()

(0, 0)


from sklearn.model_selection import train_test_split

df_Xtrain, df_Xvalid, ser_ytrain, ser_yvalid = train_test_split(
    df_Xtrain_full, ser_ytrain_full,
    test_size=0.2,
    random_state=SEED,
    stratify=ser_ytrain_full)


Xtrain_full = df_Xtrain_full.to_numpy()
Xtrain = df_Xtrain.to_numpy()
Xvalid = df_Xvalid.to_numpy()

ytrain = ser_ytrain.to_numpy().ravel()
yvalid = ser_yvalid.to_numpy().ravel()


print(f"df_train   : {df_train.shape}\n")

print(f"df_Xtrain  : {df_Xtrain.shape}")
print(f"ser_ytrain : {ser_ytrain.shape}\n")

print(f"df_Xvalid  : {df_Xvalid.shape}")
print(f"ser_yvalid : {ser_yvalid.shape}\n")

print(f"df_test    : {df_test.shape}")
print(f"ser_ytest  : This does not exist.")

df_Xtrain.head(2)

df_train   : (5634, 21)

df_Xtrain  : (4507, 77)
ser_ytrain : (4507,)

df_Xvalid  : (1127, 77)
ser_yvalid : (1127,)

df_test    : (1409, 21)
ser_ytest  : This does not exist.


model_name = 'lightgbm'
hpo_name   = 'hyperopt'


from lightgbm import LGBMClassifier


# https://github.com/Microsoft/LightGBM/blob/master/examples/python-guide/advanced_example.py
metric_profit_name = 'profit'
def metric_profit_bst(log_odds, dtrain):
    y_true = dtrain.get_label()
    y_true = np.array(y_true).astype(int)
    
    y_prob = 1. / (1. + np.exp(-log_odds))
    y_pred = np.rint(y_prob)

    profit = get_profit(y_true,y_pred)
    greater_is_better = True

    return 'profit', profit, greater_is_better


metric_profit_name = 'profit'
def metric_profit(y_true, y_prob):
    y_true = np.array(y_true).astype(int)
    y_pred = np.rint(y_prob)

    profit = get_profit(y_true,y_pred)
    greater_is_better = True

    return metric_profit_name, profit, greater_is_better


params = dict(random_state=SEED,n_estimators=1000)
model = LGBMClassifier(**params)

model.fit(df_Xtrain,ytrain,
          eval_set=(df_Xvalid, ser_yvalid),
          eval_metric = metric_profit,
          early_stopping_rounds=20,
          verbose=0,
         )

ypreds = model.predict(df_Xtest)
yprobs2d = model.predict_proba(df_Xtest)

profit = get_profit(ytest,ypreds)
print(f'test profit = ${profit:,d}')
model_eval_bin(model_name,ytest,ypreds,yprobs2d,show_plots=False)

test profit = $-21,200
              precision    recall  f1-score   support

           0       0.78      0.93      0.85      1035
           1       0.59      0.27      0.37       374

    accuracy                           0.76      1409
   macro avg       0.69      0.60      0.61      1409
weighted avg       0.73      0.76      0.72      1409

[[965  70]
 [273 101]]


ypreds[:5]

array([0, 0, 0, 0, 0])


import hyperopt
from hyperopt import hp, tpe, fmin, Trials, STATUS_OK, space_eval
from hyperopt.pyll import scope
from hyperopt.pyll.stochastic import sample
from pathlib import Path

path_trials_lgb = f'../artifacts/lgb_trials_{model_name}.joblib'

if Path(path_trials_lgb).exists():
    trials = joblib.load(path_trials_lgb)
else:
    trials = hyperopt.Trials()


show_methods(hp)


# https://github.com/Microsoft/LightGBM/blob/master/docs/Parameters.rst
# https://indico.cern.ch/event/617754/contributions/2590694/attachments/1459648/2254154/catboost_for_CMS.pdf

space_lgb_hyperopt = {
    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(1)),
    
    'max_depth'        : scope.int(hp.quniform('max_depth', 2, 32, 1)),
    'n_estimators'     : scope.int(hp.quniform('n_estimators', 100, 5000, 50)),
    'max_bin'          : scope.int(hp.quniform('max_bin', 8, 512, 1)),
    'min_data_in_leaf' : scope.int(hp.quniform('min_data_in_leaf', 1, 512, 1)),
    'min_data_in_bin'  : scope.int(hp.quniform('min_data_in_bin', 1, 512, 1)),

    'scale_pos_weight' : hp.randint('scale_pos_weight',1,20),
    # use only one of scale_pos_weight or class_weight
    #'class_weight': hp.choice('class_weight', [None, 'balanced']),

    'feature_fraction': hp.uniform('feature_fraction',0.4, 1.0),
    'subsample'       : hp.uniform ('subsample', 0.5, 1), # bagging_fraction

    # reg_alpha and reg_lambda are lambda_l1 and lambda_l2
    'lambda_l1': hp.choice('lambda_l1', [0, hp.loguniform('lambda_l1_positive', -16, 2)]),
    'lambda_l2': hp.choice('lambda_l2', [0, hp.loguniform('lambda_l2_positive', -16, 2)]),

    'min_child_weight': hp.loguniform('min_child_weight', -16, 5),
    'min_gain_to_split' : hp.quniform('min_gain_to_split', 0.1, 5, 0.01),

}


INT_PARAMS = ['n_estimators','num_boost_round','num_leaves',
              'max_depth','max_bin',
              'min_data_in_leaf','min_data_in_bin']


def lgb_objective_hyperopt_skf(params):

    global INT_PARAMS, CURRENT_BEST

    for int_param in INT_PARAMS:
        # make integer if exist
        if int_param in params:
            params[int_param] = int(params[int_param])

    # skf is more time-consuming but more stable.
    skf = StratifiedKFold(n_splits=5,random_state=SEED,shuffle=True)
    profits = []
    aucs = []
    num_best_rounds = []
    for idx_tr, idx_vd in skf.split(df_Xtrain_full, ser_ytrain_full):
        Xtr,Xvd = df_Xtrain_full.iloc[idx_tr], df_Xtrain_full.iloc[idx_vd]
        ytr,yvd = ser_ytrain_full[idx_tr], ser_ytrain_full.iloc[idx_vd]

        model = LGBMClassifier(random_state=SEED,**params)

        model.fit(Xtr, ytr,
            eval_set=[(Xvd, yvd)],
            verbose=0,
            early_stopping_rounds=100)
        
        # get best round
        e = model.evals_result_
        k0 = list(e.keys())[0]
        k1 = list(e[k0].keys())[0]
        num_best_round = len(e[k0][k1])
        num_best_rounds.append(num_best_round)

        # model predictions
        vdpreds = model.predict(Xvd)
        yvd     = yvd.to_numpy().ravel()

        auc_now = skmetrics.roc_auc_score(yvd,vdpreds)
        aucs.append(auc_now)

        profit_now = get_profit(yvd,vdpreds)
        profits.append(profit_now)

    #=============================================================
    profit         = np.mean(profits)
    profit_std     = np.std(profits)
    auc            = np.mean(aucs)
    auc_std        = np.std(aucs)
    num_best_round = np.max(num_best_rounds)
    
    # debug
    #print(f'profit : {profit:,.0f} auc : {auc:,.4f}')
    #print(params)

    # loss must be minimized, so we may need to use -ve sign.
    return {'loss'  : -profit, 'status': hyperopt.STATUS_OK,
            'profit': profit, 'profit_std': profit_std,
            'auc'   : auc,    'auc_std'   : auc_std,
            'num_best_round': num_best_round
           }


N_TRIALS = 1_000

params_best = hyperopt.fmin(fn=lgb_objective_hyperopt_skf,
            space=space_lgb_hyperopt,
            algo=tpe.suggest,
            max_evals=N_TRIALS,
            timeout= None,
            trials=trials,
            verbose=10,
            show_progressbar=True,
            rstate=np.random.RandomState(SEED)
           )

params_best =  hyperopt.space_eval(space_lgb_hyperopt, params_best)
joblib.dump(trials, path_trials_lgb)

100%|██████████| 1000/1000 [00:00<?, ?trial/s, best loss=?]

['../artifacts/lgb_trials_hyperopt.joblib']


print('\n Best parameters')
print(params_best,'\n')

print('\n Best validation results')
print(trials.best_trial['result'])
print()

# model evaluation
THRESHOLD = 0.5
best_rounds = trials.best_trial['result']['num_best_round']
# params_best['n_estimators'] = best_rounds

#============================ model eval on valid set
model = lgb.LGBMClassifier(**params_best, random_state=SEED)
model.fit(df_Xtrain, ytrain)
# vdprobs2d = model.predict_proba(df_Xvalid)
# vdpreds = np.array([1 if i[1] > THRESHOLD else 0 for i in vdprobs2d])
vdpreds = model.predict(df_Xvalid)

profit = get_profit(yvalid,vdpreds)
print(skmetrics.confusion_matrix(yvalid,vdpreds))
print(f'valid profit = ${profit:,.0f}\n')


#========================== model eval on test set
model = lgb.LGBMClassifier(**params_best, random_state=SEED)
model.fit(df_Xtrain_full, ytrain_full)

# yprobs2d = model.predict_proba(df_Xtest)
# ypreds = np.array([1 if i[1] > THRESHOLD else 0 for i in yprobs2d])
ypreds = model.predict(df_Xtest)

profit = get_profit(ytest,ypreds)
print(skmetrics.confusion_matrix(ytest,ypreds))
print(f'test profit = ${profit:,.0f}')

 Best parameters
{'feature_fraction': 0.62364717790145, 'lambda_l1': 0, 'lambda_l2': 0.00080846863064018, 'learning_rate': 0.9978224530610822, 'max_bin': 147, 'max_depth': 17, 'min_child_weight': 1.46868725453989e-05, 'min_data_in_bin': 362, 'min_data_in_leaf': 313, 'min_gain_to_split': 3.66, 'n_estimators': 1450, 'scale_pos_weight': 7, 'subsample': 0.8645278303685926} 


 Best validation results
{'loss': -70400.0, 'status': 'ok', 'profit': 70400.0, 'profit_std': 1950.3845774615836, 'auc': 0.7430315548671578, 'auc_std': 0.008208732626707885, 'num_best_round': 123}

[[455 373]
 [ 23 276]]
valid profit = $68,500

[[562 473]
 [ 33 341]]
test profit = $82,500


trials.best_trial['result']

{'loss': -70400.0,
 'status': 'ok',
 'profit': 70400.0,
 'profit_std': 1950.3845774615836,
 'auc': 0.7430315548671578,
 'auc_std': 0.008208732626707885,
 'num_best_round': 123}


params_best_82500 = {'feature_fraction': 0.62364717790145, 'lambda_l1': 0, 'lambda_l2': 0.00080846863064018, 'learning_rate': 0.9978224530610822, 'max_bin': 147, 'max_depth': 17, 'min_child_weight': 1.46868725453989e-05, 'min_data_in_bin': 362, 'min_data_in_leaf': 313, 'min_gain_to_split': 3.66, 'n_estimators': 1450, 'scale_pos_weight': 7, 'subsample': 0.8645278303685926} 


model = lgb.LGBMClassifier(random_state=SEED,**params_best)


model.fit(df_Xtrain_full,ytrain_full)
ypreds = model.predict(df_Xtest)

profit = get_profit(ytest,ypreds)
model_eval_bin(f'{model_name}+{hpo_name}',ytest,ypreds,yprobs2d,show_plots=True)
print(f"test profit       = ${profit:,d}")

              precision    recall  f1-score   support

           0       0.94      0.54      0.69      1035
           1       0.42      0.91      0.57       374

    accuracy                           0.64      1409
   macro avg       0.68      0.73      0.63      1409
weighted avg       0.81      0.64      0.66      1409

[[562 473]
 [ 33 341]]

test profit       = $82,500


time_taken = time.time() - time_start_notebook
h,m = divmod(time_taken,60*60)
print('Time taken to run whole notebook: {:.0f} hr '\
      '{:.0f} min {:.0f} secs'.format(h, *divmod(m,60)))

Time taken to run whole notebook: 0 hr 0 min 7 secs

	tenure	MonthlyCharges	TotalCharges	Contract_TotalCharges_mean	PaperlessBilling_No	PaperlessBilling_Yes	Dependents_No	Dependents_Yes	StreamingMovies_No	StreamingMovies_No internet service	StreamingMovies_Yes	SeniorCitizen_TechSupport_No_No internet service	SeniorCitizen_TechSupport_No_Yes	SeniorCitizen_TechSupport_Yes_No internet service	PhoneService_Yes	SeniorCitizen_No	SeniorCitizen_Yes	OnlineSecurity_No internet service	OnlineSecurity_Yes	Contract_Month-to-month	Contract_Two year	TechSupport_No internet service	TechSupport_Yes	DeviceProtection_No	DeviceProtection_No internet service	DeviceProtection_Yes	StreamingTV_No	StreamingTV_No internet service	StreamingTV_Yes	SeniorCitizen_Contract_No_Month-to-month	SeniorCitizen_Contract_No_Two year	SeniorCitizen_Contract_Yes_Month-to-month	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Mailed check	SeniorCitizen_Partner_No_No	SeniorCitizen_Partner_No_Yes	SeniorCitizen_Partner_Yes_No	InternetService_DSL	InternetService_Fiber optic	InternetService_No	MultipleLines_No	MultipleLines_Yes	Partner_No	Partner_Yes	OnlineBackup_No	OnlineBackup_No internet service	OnlineBackup_Yes	SeniorCitizen_Dependents_No_No	SeniorCitizen_Dependents_No_Yes	SeniorCitizen_Dependents_Yes_No	Partner_Dependents_No_No	Partner_Dependents_Yes_No	Partner_Dependents_Yes_Yes	SeniorCitizen_PaymentMethod_No_Bank transfer (automatic)	SeniorCitizen_PaymentMethod_No_Credit card (automatic)	SeniorCitizen_PaymentMethod_No_Mailed check	SeniorCitizen_PaymentMethod_Yes_Mailed check
0	36	106.05	3834.40	3683.643192	0	1	1	0	0	0	1	0	1	0	1	1	0	0	1	0	1	0	1	0	0	1	1	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	1	0	0	1	1	0	0	0	1	0	0	1	0	0
1	10	62.25	612.95	1370.923131	1	0	1	0	1	0	0	0	1	0	1	1	0	0	1	1	0	0	1	1	0	0	0	0	1	1	0	0	1	0	0	1	0	0	1	0	0	1	0	1	0	1	0	0	1	0	0	1	0	0	1	0	0	0
2	25	19.15	477.60	1370.923131	0	1	0	1	0	1	0	1	0	0	1	1	0	1	0	1	0	1	0	0	1	0	0	1	0	1	0	0	0	0	1	0	1	0	0	0	1	1	0	0	1	0	1	0	0	1	0	0	0	1	0	0	1	0
3	7	20.00	137.60	1370.923131	1	0	1	0	0	1	0	1	0	0	1	1	0	1	0	1	0	1	0	0	1	0	0	1	0	1	0	0	1	0	0	1	0	0	0	0	1	1	0	1	0	0	1	0	1	0	0	1	0	0	1	0	0	0
4	24	20.30	459.95	1370.923131	0	1	1	0	0	1	0	0	0	1	1	0	1	1	0	1	0	1	0	0	1	0	0	1	0	0	0	1	0	0	1	0	0	1	0	0	1	1	0	1	0	0	1	0	0	0	1	1	0	0	0	0	0	1

	tenure	MonthlyCharges	TotalCharges	Contract_TotalCharges_mean	PaperlessBilling_No	PaperlessBilling_Yes	Dependents_No	Dependents_Yes	StreamingMovies_No	StreamingMovies_No internet service	StreamingMovies_Yes	SeniorCitizen_TechSupport_No_No	SeniorCitizen_TechSupport_No_No internet service	SeniorCitizen_TechSupport_No_Yes	SeniorCitizen_TechSupport_Yes_No	SeniorCitizen_TechSupport_Yes_No internet service	SeniorCitizen_TechSupport_Yes_Yes	PhoneService_No	PhoneService_Yes	SeniorCitizen_No	SeniorCitizen_Yes	OnlineSecurity_No	OnlineSecurity_No internet service	OnlineSecurity_Yes	Contract_Month-to-month	Contract_One year	Contract_Two year	TechSupport_No	TechSupport_No internet service	TechSupport_Yes	DeviceProtection_No	DeviceProtection_No internet service	DeviceProtection_Yes	StreamingTV_No	StreamingTV_No internet service	StreamingTV_Yes	SeniorCitizen_Contract_No_Month-to-month	SeniorCitizen_Contract_No_One year	SeniorCitizen_Contract_No_Two year	SeniorCitizen_Contract_Yes_Month-to-month	SeniorCitizen_Contract_Yes_One year	SeniorCitizen_Contract_Yes_Two year	PaymentMethod_Bank transfer (automatic)	PaymentMethod_Credit card (automatic)	PaymentMethod_Electronic check	PaymentMethod_Mailed check	SeniorCitizen_Partner_No_No	SeniorCitizen_Partner_No_Yes	SeniorCitizen_Partner_Yes_No	SeniorCitizen_Partner_Yes_Yes	InternetService_DSL	InternetService_Fiber optic	InternetService_No	MultipleLines_No	MultipleLines_No phone service	MultipleLines_Yes	Partner_No	Partner_Yes	OnlineBackup_No	OnlineBackup_No internet service	OnlineBackup_Yes	SeniorCitizen_Dependents_No_No	SeniorCitizen_Dependents_No_Yes	SeniorCitizen_Dependents_Yes_No	SeniorCitizen_Dependents_Yes_Yes	Partner_Dependents_No_No	Partner_Dependents_No_Yes	Partner_Dependents_Yes_No	Partner_Dependents_Yes_Yes	SeniorCitizen_PaymentMethod_No_Bank transfer (automatic)	SeniorCitizen_PaymentMethod_No_Credit card (automatic)	SeniorCitizen_PaymentMethod_No_Electronic check	SeniorCitizen_PaymentMethod_No_Mailed check	SeniorCitizen_PaymentMethod_Yes_Bank transfer (automatic)	SeniorCitizen_PaymentMethod_Yes_Credit card (automatic)	SeniorCitizen_PaymentMethod_Yes_Electronic check	SeniorCitizen_PaymentMethod_Yes_Mailed check
4555	16	19.75	294.90	1370.923131	1	0	1	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	1	0	1	0	0	0	1	0	0	1	0	0	1	0	1	0	0	0	0	0	0	1	0	0	1	0	0	0	0	0	1	1	0	0	1	0	0	1	0	1	0	0	0	1	0	0	0	0	1	0	0	0	0	0	0
3379	72	64.70	4746.05	3683.643192	0	1	1	0	0	0	1	0	0	1	0	0	0	1	0	1	0	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	1	0	0	0	0	0	1	0	0	1	0	0	1	0	0	0	1	0	0	1	0	0	1	1	0	0	0	0	0	1	0	0	0	1	0	0	0	0	0

	0	1	2	3
0	absolute_import	normal	qloguniform	randint
1	choice	pchoice	qnormal	uniform
2	lognormal	qlognormal	quniform	uniformint
3	loguniform

Modelling Customer Churn using LightGBM

Load the libraries

Colab¶

Useful Scripts

Load the Data

Data Processing

Data Processing¶

Data Types¶

Train and Test Data¶

Numerical and Categorical Features¶

Custom Features¶

Train Validation Split¶

Modelling

LightGBM HPO Using Hyperopt¶

Model Evaluation

Time Taken

	customerID	gender	Partner	Dependents	tenure	PhoneService	MultipleLines	InternetService	OnlineSecurity	OnlineBackup	DeviceProtection	TechSupport	StreamingTV	StreamingMovies	Contract	PaperlessBilling	PaymentMethod	MonthlyCharges	TotalCharges	Churn
0	1621-YNCJH	Female	Yes	No	36	Yes	Yes	Fiber optic	Yes	Yes	Yes	Yes	No	Yes	Two year	Yes	Credit card (automatic)	106.05	3834.4	No
1	7143-BQIBA	Male	No	No	10	Yes	No	DSL	Yes	No	No	Yes	Yes	No	Month-to-month	No	Bank transfer (automatic)	62.25	612.95	No
5632	0862-PRCBS	Female	Yes	Yes	68	Yes	Yes	Fiber optic	No	Yes	No	Yes	Yes	Yes	Two year	Yes	Credit card (automatic)	103.75	7039.45	No
5633	4656-CAURT	Male	No	No	69	Yes	Yes	No	No internet service	No internet service	No internet service	No internet service	No internet service	No internet service	Two year	No	Bank transfer (automatic)	23.95	1713.1	No